针对某山区削坡填沟加筋高陡边坡开展的三组离心模型试验,建立基于离心模型试验尺寸的数值模型,模拟离心试验加载运行过程,研究不同填料强度下边坡的变形和破坏模式,以及筋材应力-应变分布规律,并与离心试验成果进行对比分析,进而开展筋材分布的拓展研究。研究结果表明:坡体下部填料强度不足时,在上覆荷载作用下,坡体下部发生侧向挤出,引起上部坡体底层筋材进入屈服阶段,上部加筋体内部坡脚至H/3(H为上、下级边坡高度)处形成潜在滑动面;加筋边坡筋材内力在平台处发生突变,数值分析结果表明随着坡高的增加,筋材最大轴力下移;适当增加下部坡体筋材长度可提高边坡整体稳定性,通过对各级边坡H/3范围的筋材进行加密,筋材应力分布整体得到优化,数值分析结果可为优化加筋边坡设计提供重要的理论依据。
Abstract
Three groups of centrifugal model tests were carried out on a high and steep reinforced slope formed by cutting mountain and filling valley. Numerical models which simulate the loading process of centrifugal tests were established based on the centrifugal model tests. The deformation, stress-strain distribution of reinforcement and failure modes of reinforced slope were studied under different foundation strengths. The results of numerical simulation and centrifugal test were compared and analyzed for further research on the distribution of reinforcement. Results revealed that lateral extrusion of the lower slope was observed in the presence of increasing overlying load, which led to the yield of the bottom reinforcement beneath the upper part of slope when the strength of the lower filler was insufficient. Potential sliding surface was formed from the foot of the inner slope to H/3. The internal force of reinforcement changed abruptly at the platform, and the position of maximum axial force of reinforcement moved downwards with the increase of slope height. The stability of the slope can be improved by increasing the length of geosynthetics in the lower slope appropriately. The stress distribution of reinforcement can be optimized by densifying the geosynthetics in the H/3 range of reinforced slopes at all levels. The numerical analysis results provide a theoretical basis for optimizing the design of reinforced slopes.
关键词
加筋边坡 /
离心模型试验 /
数值分析 /
PLAXIS /
拓展研究
Key words
reinforced slope /
centrifugal model test /
numerical analysis /
PLAXIS /
extented research
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基金
国家重点研发计划项目(2017YFC0405001);广州市多要素城市地质调查项目(DD20190219);广州市科技计划项目(201604016063);国家自然科学基金青年基金项目(51709017);广东省城市感知与监测预警企业重点实验室基金项目(2020B121202019)
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